Por favor, use este identificador para citar o enlazar este ítem:
https://doi.org/10.1007/s10596-024-10285-y
Twittear
Título: | Analysis of the hyperparameter optimisation of four machine learning satellite imagery classification methods |
Fecha de publicación: | 5-abr-2024 |
Editorial: | Springer |
Cita bibliográfica: | Computational Geosciences, 2024 |
ISSN: | Print: 1420-0597 Electronic: 1573-1499 |
Materias relacionadas: | CDU::9 - Geografía e historia |
Palabras clave: | Machine learning LULC Convolutional neuronal networks Random forest Support vector machines Hyperparameter optimisation |
Resumen: | The classification of land use and land cover (LULC) from remotely sensed imagery in semi-arid Mediterranean areas is a challenging task due to the fragmentation of the landscape and the diversity of spatial patterns. Recently, the use of deep learning (DL) for image analysis has increased compared to commonly used machine learning (ML) methods. This paper compares the performance of four algorithms, Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolutional Network (CNN), using multi-source data, applying an exhaustive optimisation process of the hyperparameters. The usual approach in the optimisation process of a LULC classification model is to keep the best model in terms of accuracy without analysing the rest of the results. In this study, we have analysed such results, discovering noteworthy patterns in a space defined by the mean and standard deviation of the validation accuracy estimated in a 10-fold cross validation (CV). The point distributions in such a space do not appear to be completely random, but show clusters of points that facilitate the discovery of hyperparameter values that tend to increase the mean accuracy and decrease its standard deviation. RF is not the most accurate model, but it is the less sensitive to changes in hyperparameters. Neural Networks, tend to increase commission and omission errors of the less represented classes because their optimisation lead the model to learn better the most frequent classes. On the other hand, RF and MLP prediction layers are the most accurate from a general qualitative point of view. |
Autor/es principal/es: | Alonso Sarría, Francisco Valdivieso Ros, Carmen Gomariz Castillo, Francisco |
Facultad/Departamentos/Servicios: | Facultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Geografía |
Versión del editor: | https://link.springer.com/article/10.1007/s10596-024-10285-y |
URI: | http://hdl.handle.net/10201/141101 |
DOI: | https://doi.org/10.1007/s10596-024-10285-y |
Tipo de documento: | info:eu-repo/semantics/article |
Número páginas / Extensión: | 21 |
Derechos: | info:eu-repo/semantics/openAccess Atribución 4.0 Internacional |
Descripción: | © The Author(s) 2024. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/ This document is the Published version of a Published Work that appeared in final form in Computational Geosciences. To access the final edited and published work see https://doi.org/10.1007/s10596-024-10285-y |
Aparece en las colecciones: | Artículos: Geografía |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Alonso-Sarria_etal_ComGeos.pdf | Published Version | 9,99 MB | Adobe PDF | Visualizar/Abrir |
Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons